Radial diffusivity reflects general decline rather than specific cognitive deterioration in multiple sclerosis

Advanced structural brain imaging techniques, such as diffusion tensor imaging (DTI), have been used to study the relationship between DTI-parameters and cognitive scores in multiple sclerosis (MS). In this study, we assessed cognitive function in 61 individuals with MS and a control group of 35 healthy individuals with the Symbol Digit Modalities Test, the California Verbal Learning Test-II, the Brief Visuospatial Memory Test-Revised, the Controlled Oral Word Association Test, and Stroop-test. We also acquired diffusion-weighted images (b = 1000; 32 directions), which were processed to obtain the following DTI scalars: fractional anisotropy, mean, axial, and radial diffusivity. The relation between DTI scalars and cognitive parameters was assessed through permutations. Although fractional anisotropy and axial diffusivity did not correlate with any of the cognitive tests, mean and radial diffusivity were negatively correlated with all of these tests. However, this effect was not specific to any specific white matter tract or cognitive test and demonstrated a general effect with only low to moderate individual voxel-based correlations of <0.6. Similarly, lesion and white matter volume show a general effect with medium to high voxel-based correlations of 0.5-0.8. In conclusion, radial diffusivity is strongly related to cognitive impairment in MS. However, the strong associations of radial diffusivity with both cognition and whole brain lesion volume suggest that it is a surrogate marker for general decline in MS, rather than a marker for specific cognitive functions.

concluded that information processing speed (IPS) is correlated with DTI measures of several white matter bundles, mainly in the corpus callosum. However, many studies used the Paced Auditory Serial Addition Test (PASAT) to measure IPS. Because PASAT is poorly tolerated and the Symbol Digit Modalities Test (SDMT) is easier to administer, SDMT has replaced PASAT as the most commonly used cognitive task to assess IPS in the past decade 3 . Comparing brain microstructure between subjects poses two problems: dealing with spatial anatomical variability and correcting for multiple comparisons. The field has evolved from lowering the significance threshold in voxel based analysis 13 according to multiple comparisons methods, such as Bonferroni 14 , to cluster-based analysis with set thresholds 15 and finally threshold-free methods, such as threshold-free cluster enhancement (TFCE) 16 . A recent review article by Manca et al. 12 analysed brain processing speed in relation to DTI states. The authors showed that past literature presented a high variability because various older multiple comparisons approaches were used and thus results were inconclusive, creating a need for further research of the DTI parameters within MS. Other studies using TFCE 17,18 had small sample sizes and limited scope by using only one DTI parameter or cognitive test. Meijer et al. 18 did not experience these limitations but only investigated a dichotomization of the cognitive status (i.e., cognition impaired versus preserved), instead of studying separate cognitive domains.
In this study, we revisit the hypothesis that DTI parameters are correlated with cognition. Furthermore, by including cognitive tests that evaluate the most affected cognitive domains in MS, we will assess whether changes in DTI parameters are specific to certain cognitive domains. We perform a group analysis comparing the DTI parameters of 61 PwMS with 35 healthy subjects (HS), using tract-based spatial statistics (TBSS) 19,20 . Second, we assess each cognitive domain and its relation to the DTI parameters in our complete study sample throughout the whole brain, by combining the advantages of applying a TFCE as a cluster-based analysis and a voxel-based analysis, similar to Bernabéu-Sanz 2021 21 .

Results
Demographics, clinical, and neuropsychological data. Our study included 61 PwMS and 35 HS. The characteristics of each group are presented in Table 1. Age and sex were similar in both groups, but there was a statistically significant difference in terms of education level, fatigue, and depression. While both groups performed similarly on verbal and visual memory tests and the combined test for processing speed and attention, PwMS scored significantly lower on tests for processing speed, verbal fluency, and hand motricity.
Analysis of diffusion tensor parameters: group differences. In a first analysis, we compared the diffusion tensor parameters of the PwMS group to those of the HS group; the results are illustrated in Fig. 1. PwMS showed a general decrease in fractional anisotropy (FA), across the whole white matter and an increase in mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) in all regions. The results of the cluster analysis of the permutation testing are presented in Table 2. Only one large cluster was found for each of the diffusion tensor parameters.
Correlation of diffusion tensor parameters with cognitive scores. In a second analysis, we assessed the relation between the diffusion tensor and neuropsychological parameters. First, we identified clusters that were significantly correlated with the different cognitive tests through TBSS and TFCE. The visualisation of these intermediate results is available in an online repository (https:// neuro vault. org/ colle ctions/ LYEGO WBT/) aligned to the MNI-space in Neuroimaging Informatics Technology Initiative (NIfTI) format. In the next www.nature.com/scientificreports/ step, the correlations between DTI parameters of the voxels within these clusters and the different cognitive parameters were assessed using the significant clusters as mask for the calculations, which is shown in Fig. 2. Finally, these correlations were evaluated using a white matter bundle atlas. There were no significant correlations between FA and AD and any of the assessed variables. MD and RD were correlated with the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test-II (CVLT), Brief Visuospatial Memory Test-Revised (BVMT), and the Controlled Oral Word Association Test (COWAT) but only the SDMT correlation passed the correction for multiple comparisons. The strongest correlation for each white matter bundle is listed in Table 3.

Correlation of diffusion tensor parameters with volumetric parameters and EDSS.
Combining all T2 FLAIR hyperintensity lesion maps, we created a heat map to indicate the probability that a lesion was detected in a certain location in our dataset (see Fig. 3). Furthermore, we assessed the association between tensor parameters and lesions volume, WM volume and EDSS. The intermediate results are provided in the online repository: https:// neuro vault. org/ colle ctions/ LYEGO    Table 3. Strongest observed correlations between the diffusion tensor parameters and neuropsychological z-scores for each bundle of the IIT white matter bundle atlas. Bundles presenting no significant correlations are indicated with a backslash, and a red (negative) and green (positive) colour palette is used to ease visual interpretation of the r-values strength and direction. The r column represents the strength of the correlation and the − LOG(p) shows the significance on the inverse logarithmic scale (the cut-off p value of 0.001 is − LOG10(p) = 3). The volume of the shared region between the considered cluster and the tracks is shown in mm 3 Fig. 4. The strongest correlations for each white matter bundle from the IIT atlas are listed in Table 4. We found that only FA was significantly related to the white matter volume, while AD, MD, and RD were significantly associated with lesion volume. Also, MD and RD were significantly associated with the EDSS, but the correlations with MD did not pass correction for multiple comparisons.

Discussion
In this paper, we compared DTI parameters between MS and HS and observed a significant decrease in FA in PwMS. This likely reflects a decrease in microstructural integrity 22,23 . Furthermore, there was an increase in AD and RD in PwMS compared to HS. These parameters have been related to demyelination in ex vivo 24 and mouse studies 25,26 . Since MD can be expressed as a linear combination of RD and AD 23 , it is linked to the same biophysical properties and therefore also increased in PwMS. The associations between AD and RD and biophysical properties have been questioned as generalizations, in part because these parameters can be altered in pathological brain tissue without reflecting the organization of the underlying tissue. Numerical simulations of the DTI parameters with crossing fibres associated an increase in AD and RD with demyelination 27 . With axonal degeneration, we expect FA and AD to decrease while RD increases; with demyelination we expect FA to decrease, AD to be unaffected and RD to increase 28 . Despite the need for a careful interpretation, our findings indicate a clear pathology-induced alteration of the brain structure in PwMS compared to the HS. We observed a correspondence between the MS lesions map (available in Fig. 3) and regions showing significant group differences of the DTI parameters. In a second step, we looked at our main research question, the relation between DTI-parameters and cognitive scores. On this subject, the literature was highly variable and inconclusive 12 . Bernabéu-Sanz 2021 21 indicated thalamic atrophy as the main source of cognitive impairment in general and found associations between abnormalities in the occipital projection fibers of the corpus callosum and the SDMT in mildly disabled patients with relapsing-remitting multiple sclerosis (RRMS). Riccitelli 2019 29 also studied RRMS and associated the PASAT and SDMT with widespread abnormalities in DTI in several regions, but the strength of these associations was not reported. Similarly, studies that compared cognitively impaired and cognitively preserved persons with MS (Meijer 18 and Zhao 30 ) or studies assessing a specific brain area (i.e., the thalamus in Benedict 31 and corpus callosum in Sun 32 ) have reported similar results. However, the question arises as to whether these associations are specific to information processing speed or reflect a more general (cognitive) decline.
We observed that MD and RD were correlated with the SDMT. Furthermore, the TBSS analysis showed one large significant cluster when correlations were found. This is in correspondence with Riccitelli et al. 29 , who found correlations between SDMT and MD in all bundles, except for the right cingulum, right fornix, and left posterior thalamic radiata. The association of SDMT with numerous pathways is not surprising. A functional MRI independent component analysis of an SDMT task has found that information processing speed is related to the visual network, cerebellum network, motor network, auditory network, visuospatial processing and reasoning network, and the default mode network 33 . At the voxel-level, we found low to medium correlations, below 0.6, between the DTI parameters and cognitive tests in the PwMS and HS group. The corpus callosum consistently shows the greatest overlap and a high correlation with cognition compared to the other tracts. Damage to the callosal tracts leads to dissociation of the connections between the hemispheres and has been associated with   34,35 . Furthermore, diffusion tensor parameters in the corpus callosum have been consistently associated with processing speed 12,32 . We did not observe any association between FA/AD and the different cognitive tests assessed through nonparametric permutation on the TBSS tracts. This is in line with previous studies, which reported weak 12,30 or absent 12,32 correlations. Other studies only found correlations with the PASAT 12 , but this test only has moderate sensitivity to cognitive deficits and has been replaced by the SDMT in clinical practice 3 . Some studies found correlations with other cognitive tests, but only in the corpus callosum 30,32 . Finally, a decreased FA has been observed in cognitively impaired PwMS compared to those with intact cognition 18 .
Based on functional connectivity studies, our expectation was that the different cognitive tests would show more distinctive correlation patterns with MD/RD, instead of a rather homogeneous correlation strength. SDMT, for instance, measures information processing speed and is linked to several brain regions (Silva et al. 36 found 19 pairs of cortical regions in relation to the information processing network), while the CVLT measures verbal memory, which is predominantly associated with the left medial temporal lobe, the right hippocampus, and right frontal lobe on fMRI 37 . This discrepancy between our expected and observed findings could mean that MD/RD are markers of more general damage in MS, rather than specific markers of cognitive deterioration.
We examined the association of whole brain lesion volume and white matter volume with the tensor-based parameters. In this analysis much stronger correlations, with r values between 0.45 and 0.83, were observed for all bundles. Additionally, the brain regions that show significant correlations between MD, RD, AD and lesion volume are larger than the regions that showed significant correlations between MD, RD and all the cognitive tests. Considering that RD and MD show similar correlation patterns with cognitive tests, white matter, lesion volume, and EDSS scores, we suggest that MD and RD are plausible surrogate markers for the general decline of brain performance due to MS rather than specific markers for cognitive decline. Since AD was not correlated with any of the neuropsychological tests, the correlation between MD and the different cognitive tests seems to be driven by RD 27 . As such, RD appears to be of particular interest in assessing cognitive impairment in multiple sclerosis.
Several limitations must be considered: The two groups were matched for age and sex, but not for education level. Despite their lower education level, which is usually associated with a lower cognitive score 38 , the PwMS group did not perform significantly worse on 3 of the 6 cognitive tests (CVLT, BVMT, and Stroop). Repeating the analysis with a slightly smaller cohort of MS patients that was matched for education (p > 0.10) did not affect our results. All PwMS were recruited either in an academic hospital (UZ Brussel) or a reference centre (National This learning effect will decrease the sensitivity of our analysis. Additionally, our study has a cross-sectional design. A longitudinal study design on cognitive deterioration in MS could provide more insight into the associations between cognition and structural connectivity measures. Furthermore, medication and MS type were not considered in this study. Since we used a cross-sectional design, and therefore only looked at a fixed situation and not to the evolution, it could be neglected. For a longitudinal study, these aspects however cannot be neglected. Moreover, there are limitations inherent to the methods used for image processing and analysis: First, TBSS was designed to circumvent the partial volume effect, but the registration and projection of all subjects to one skeleton remains a critical step, where atrophy and lesions will inevitably worsen the sensitivity to construct the tract in PwMS 19 .
As the projection depends on the morphology, any surrounding lesions or atrophy can introduce voxel misassignment. Second, the TFCE method includes a spatial smoothing before demonstrating the significance of clusters. Thus not guaranteeing the correlations of any voxel within the cluster. However, this should reduce false negative results on a voxel level. Finally, we used the maximum correlation as an approximation of the relative importance of the cluster. However, this intrinsically introduces a bias, namely, to potentially overestimate the importance of the cluster.
In conclusion, RD is generally considered a robust marker of demyelination and is strongly correlated with cognitive markers. Our results indicate that correlations with cognitive impairment are not limited to specific tracts, but rather affect all the white matter. Furthermore, these correlations are not specific to distinct cognitive domains but rather reflect the overall damage to the central nervous system caused by MS.

Methods
Compliance with ethical standards. Ethical approval was provided by the ethical committee: "commissie medische ethiek van MS Center Melsbroek" on 12 February 2015 and by the "medical ethics committee UZ BRUSSEL-VUB" on 25 February 2015 (B.U.N. 143201423263). Data analysis was carried out according to these study protocols and following the applicable local regulations. Written informed consent was obtained from all participants prior to inclusion. Table 4. Strongest observed correlation in the PwMS group between diffusion tensor parameters and volumetric parameters and EDSS for each bundle of the IIT white matter bundle atlas. Bundles without any significant correlations are indicated with a backslash and a red (negative) and green (positive) colour palette is used to facilitate visual interpretation of the R values. The r column represents the strength of the correlation and − LOG(p) shows the significance on the inverse logarithmic scale (cut-off p value of 0.001 is − LOG10(p) = 3). The volume of the shared region between the considered cluster and the tracks is shown in mm 3 . MD mean diffusivity, RD radial diffusivity, BVMT brief visuospatial memory test-revised, COWAT controlled oral word association test, SDMT symbol digit modalities test, CVLT California verbal learning test-II. Ziekenhuis Brussel-VUB and the HS were recruited from hospital staff and acquaintances of the PwMS. All study participants were between 18 and 65 years old and were able to undergo MRI (absence of contraindications, eg pacemaker, prosthesis). Only patients with a diagnosis of definite MS according to the revised McDonald criteria 39 and an Expanded Disability Status Scale score 40 (EDSS) below 6.0 were included. HS were excluded if they suffered from any neurological condition or if they had first-degree relatives with MS. In a timeframe of maximum 3 days, two testing sessions were organised, one to obtain demographical and clinical data as well as cognitive and other testing results and one to acquire a sequence of MR images. The demographical and clinical data included age, sex, education level and disease duration. Neuropsychological testing included the Brief international cognitive assessment for MS (BICAMS) test battery 41,42 which consist of the Symbol Digit Modalities Test (SDMT, information processing speed) 41 , California Verbal Learning Test-II (CVLT, verbal memory and learning) 41 , and the Brief Visuospatial Memory Test-Revised (BVMT, visual memory and learning) 41 . The Controlled Oral Word Association Test (COWAT, verbal fluency) 43 and the Stroop-test (Stroop, processing speed and attention) 44 were also carried out. The cognitive scores were z-transformed for future analysis using the HS group as a reference. This allowed us to correct for age, education level and sex of the subjects, we refer to Costers et al. 42 for more details. Image processing. MRtrix3 45 and FSL 46-48 software packages were used for processing and MATLAB 2019a was used for further statistical analysis. The diffusion-weighted images (DWI) were denoised 49 and the Gibbs-ringing effect 50 was removed before estimating the susceptibility-induced field, based on the B0 and reverse phase-encoded image 51 . Correction for eddy currents 52 and movement in the diffusion data was carried out using outlier replacement 53 (using 3 standard deviations as a threshold due to the low number of acquired directions), the slice-to-volume motion correction 54 (with 8 iterations and a degree of freedom of 8) and susceptibility-by-movement correction 55 . We performed bias field correction through the N4 BiasFieldCorrection algorithm from the advanced normalization tools (ANTs) toolkit 56 . This algorithm is an improved variation of the nonparametric nonuniform normalization retrospective bias correction algorithm. From these images, the diffusion tensor was estimated using the weighted linear least-squares estimation 57 . Different maps of the following tensor-derived parameters were created: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). MD is related to AD and RD through the following formula:

Scientific Reports
We then applied tract-based spatial statistics (TBSS) 20 and aligned all images to the FMRIN58_FA standardspace 58 image. Subject FA-images were skeletonised using the IIT Human Brain Atlas (v.5.0) 59 templates (IIT-FAskeleton) and the IIT variation of the TBSS-script. A template rather than a study derived skeleton was chosen due to the concerns about pathological influences on the projection to the skeleton 19 . These concerns cannot entirely be eliminated but are limited to a subject-level rather than the group-level. For the non-FA parameters, we reused the non-linear registration from the FA-images to the skeletonised map with the IIT version of the TBSS script.
The lesion maps, the volume of lesions and the volume of white matter (WM) were obtained using the icobrain 3.1 software, previously known as MSmetrix 60 . The lesion maps and the diffusion tensor maps were transformed into the MNI space with the ANTS registration toolbox 61 and using the 2 mm standard template available in FSL as reference 58 . Statistical analysis. In a first analysis, we looked at the differences between PwMS and HS by performing a nonparametric permutation, using the FSL randomise tool 62 with 2000 permutations and the Threshold-Free Cluster Enhancement (TFCE) option, which generates results that are corrected for multiple comparisons across space. A threshold of 0.05 for statistical significance was used for the corrected p-value. This resulted in a binary 3D image indicating the spatial regions in which the tensor parameters are significantly different between PwMS and HS.
In a second analysis, we assessed possible associations between tensor parameters and neuropsychological tests and the association between tensor parameters and the lesion and WM volume. We used a general linear model: the neuropsychological test z-scores were used as weights in the design matrix of TBSS and similarly to the first analysis, we obtained the significant clusters with TFCE and used a threshold for the corrected p-values of 0.05, which we further lowered to 0.001 to account for the multiple comparisons (as each tensor parameter was compared to six parameters). www.nature.com/scientificreports/ However, the cluster enhancement technique gains spatial specificity at the cost of losing all the information about the cluster strength. For this reason, we added a cluster analysis and used a voxel-wise analysis within the found clusters to gain information about the strength of these clusters. Associations between neuropsychological test z-scores and the values of the skeletonized tensor parameters were assessed through Pearson's correlations in each voxel of the clusters.
As the last step of the second analysis, we used an atlas of white matter bundles, the IIT Human Brain Atlas (v.5.0) 59 , and retained the strongest correlation (highest r-value) with a p-value lower than 0.001 (to compensate for multiple comparisons) for each bundle in the atlas. The second analysis was carried out for HS and PwMS together, as we assumed that the correlation effect is independent of the presence of disease. Furthermore, by including both HS and PwMS, we can assess the correlation between DTI measures and cognition in a sample with a wide range of cognitive performances. The analysis was performed for only the PwMS as well but will only be included in the repository (https:// neuro vault. org/ colle ctions/ LYEGO WBT/) and will not be discussed in this manuscript.
Permission to reproduce material from other sources. All illustrations are derived from the data used in this paper and were created only for this manuscript with the FSLeyes toolbox or in MATLAB (2021). No material was copied from another source.

Data availability
Public sharing of the data used in this manuscript is not possible due to privacy and ethical restrictions. The data that support the findings of this study are available on request to the corresponding author.